Large-scale online feature selection for ultra-high dimensional sparse data

• Main Authors: WU, Yue, HOI, Steven C. H., MEI, Tao, YU, Nenghai
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Feature selection; Second-order online learning; Sparsity; Ultra-high dimensionality; Databases and Information Systems; Numerical Analysis and Computation; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/3781; https://ink.library.smu.edu.sg/context/sis_research/article/4783/viewcontent/Large_Scale_Online_Feature_Selection_Ultra_high_2017_afv.pdf
• Institution: Singapore Management University

Feature selection (FS) is an important technique in machine learning and data mining, especially for large scale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world. As real data may...